Secondly, inspired by the approach of click models [1]}, we also propose the approximate maximum likelihood estimator \(p^*_n\) , obtained via iterative application of Newton's method [2]} to maximize the log-likelihood of the data \(L\) :
\(\begin{split}L = \sum _{i=1}^n {} & {} r_i \ln (\lambda _i p^*_n + (1-\lambda _i) \bar{p}_{i-1}) \\[-1.5ex]&+ (1-r_i)\ln (1- (\lambda _i p^*_n + (1-\lambda _i) \bar{p}_{i-1})).\end{split}\) 
